code: def convert_batch(
      batch, backend="pytorch", is_cuda=False, idim=40, odim=5
  ):
      ilens = np.array([x[1]["input"][0]["shape"][0] for x in batch])
      olens_tgt = np.array(
          [x[1]["output"][0]["shape"][0] for x in batch]
      )
      olens_src = np.array(
          [x[1]["output"][1]["shape"][0] for x in batch]
      )
      xs = [
          np.random.randn(ilen, idim).astype(np.float32)
          for ilen in ilens
      ]
      ys_tgt = [
          np.random.randint(1, odim, olen).astype(np.int32)
          for olen in olens_tgt
      ]
      ys_src = [
          np.random.randint(1, odim, olen).astype(np.int32)
          for olen in olens_src
      ]
      is_pytorch = backend == "pytorch"
      if is_pytorch:
          xs = pad_list([torch.from_numpy(x).float() for x in xs], 0)
          ilens = torch.from_numpy(ilens).long()
          ys_tgt = pad_list(
              [torch.from_numpy(y).long() for y in ys_tgt], -1
          )
          ys_src = pad_list(
              [torch.from_numpy(y).long() for y in ys_src], -1
          )
  
          if is_cuda:
              xs = xs.cuda()
              ilens = ilens.cuda()
              ys_tgt = ys_tgt.cuda()
              ys_src = ys_src.cuda()
      else:
          raise NotImplementedError
  
      return xs, ilens, ys_tgt, ys_src
  
imports: from __future__ import division
  import argparse
  import importlib
  import os
  import tempfile
  from test.utils_test import make_dummy_json_st
  import chainer
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.utils.training.batchfy import make_batchset
label_desc: Use image node_id 3 for calling a global function with example usage: convert_batch(batch, backend, is_cuda, idim, odim) and returns: xs,  ilens,  ys_tgt,  ys_src